Effect of diabetes self-management education in type 2 diabetes management - a systematic umbrella review of meta-analysis

Abstract

Background

Type 2 diabetes is a chronic metabolic health condition that affects millions of people worldwide. Diabetes self-management education (DSME) is an effective intervention approach and the potential to enhance the health status of diabetic patients and mitigate complications to the greatest possible extent. However, no systematic synthesis or comprehensive report has been conducted to date on the impact of DSME in patients with diabetes. This review aimed to identify existing evidence on DSME care for diabetes management and the extent to which interventions are included in the evidence base.

Methods

PubMed, EMBASE, Web of Science, and Cochrane library databases were searched from inception to the December 31, 2025. Systematic reviews with meta-analyses of randomized controlled trials were included, reporting an effect size to calculate the association between DSME and the outcomes related to HbA1c. Quality of each review was appraised using a Measurement Tool to Assess Systematic Reviews (AMSTAR).

Results

A total of 12 meta-analyses were included. In this study, we suggested that DSME will leads to a decrease in HbA1c (effect size [ES] = − 0.34%, 95% confidence interval [CI]: − 0.43% to − 0.25%). The associations of self-management intervention (ES = − 0.35%, 95%CI: − 0.50% to − 0.20%) and technology-based intervention (ES = − 0.33%, 95%CI: − 0.46% to − 0.20%) with lower HbA1c levels were observrd. Overall quality of the included reviews was high or moderate.

Conclusions

This umbrella review consolidates the existing evidence demonstrating that DSME has a significant positive impact on HbA1c levels, among individuals with type 2 diabetes. Future research should prioritize the development and implementation of integrated DSME models to enhance their effectiveness across various health outcomes and ensure adaptability to diverse population needs.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12902-026-02183-4.

Background

Type 2 diabetes is a chronic metabolic health condition that affects millions of people worldwide. In 2024, diabetes affected 11.11% of the global adult population, corresponding to 589 million adults, and is projected to affect 12.96% and 853 million people aged 20 − 79 years by 2050 [1]. Individuals with type 2 diabetes account for over 90% of all diabetes cases, and their associated complications, physical and mental health issues, and increased mortality impose a substantial burden on the healthcare system [2, 3].

Diabetes self-management education (DSME) is a critical and continuous intervention that empowers individuals with diabetes to improve their self-care skills, knowledge, and practices [4, 5]. It has been shown to effectively reduce mortality rates and enhance the quality of life among diabetic patients [6, 7]. Moreover, glycemic control, as reflected by HbA1c levels, offers a reliable assessment of hyperglycemia. Furthermore, HbA1c serves as a significant predictor of future diabetes-related complications [8, 9]. Therefore, more objective results in diabetes management can be obtained by measuring HbA1c.

Previous research suggests that DSME has a significant positive impact on HbA1c levels [7, 10]. However, there are several lines of evidence showing different results [11, 12]. Furthermore, Rohilla demonstrated that diabetes self-Management education and support improved outcomes for children and young adults with type 1 diabetes [13]. To the best of our knowledge, there has been no umbrella review (UR) comprehensively verifying the relationship between DSME and HbA1c levels after the studies of Kavookjian [7] and Ravi [10]. Therefore, given the controversial findings and the current lack of high-level evidence on this issue, we conducted the present study to further explore and investigate the topic in greater depth.

Methods

Literature search

This study was reported and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis [14] and Meta-Analysis of Observational Studies in Epidemiology guidelines [15].Two authors (LM and W-SZ) independently conducted a comprehensive literature screening. We systematically searched PubMed, EMBASE, Web of Science, and Cochrane Library databases from 2020 to 2025 with the final update in December 2025, without any language restrictions. The search strategy included terms, phrases, and subject headings related to research question concepts (e.g., self-management education, self-management, education, Diabetes Mellitus, Type 2, type 2 diabetes, type 2 diabetes Mellitus, systematic review, systematic reviews, meta analysis, meta-analysis and systematic overview). The systematic approach to the literature search, including the specific keywords applied, is presented in Supplementary Table S1. Moreover, to ensure the comprehensiveness of the literature review, we manually examined the reference lists of all relevant reviews and meta-analyses for additional relevant studies.

Eligibility criteria

We restricted our inclusion to systematic reviews incorporating meta-analyses of randomized controlled trials (RCTs). These studies specifically investigated the impact of self-management education interventions on patients with T2DM and reported outcomes related to HbA1c. For the purposes of this review, self-management education interventions included peer-based intervention, self-management intervention and technology-based intervention [7]. Besides, only studies with subjects older than 18 years of age were included. Given the substantial differences in intervention strategies between adults and children or adolescents [16], and with this meta-analysis focusing on adult T2DM patients, such pediatric-oriented studies did not align with our research scope.​.

Exclusion criteria are as follows: (1) systematic reviews without quantitative analysis; (2) systematic reviews with meta-analyses not reporting comprehensive data for re-analysis, such as effect sizes (hazard ratio [HR] or relative risk [RR]) and corresponding 95% confidence intervals (CIs); (3) systematic reviews with mixed patient populations (type 1 and type 2 diabetes) or gestational diabetes.

Two independent authors (LM and RW) conducted the initial screening of titles and abstracts. In cases of any discrepancies during the literature screening process, a third author (Y-QZ) was consulted to reach a consensus decision.

Data extraction

There are 6 trained authors (LM, W-SZ, RW, S-SH, S-YC, and L-HS) matched in pairs, and independently collected the information from each eligible study. All disagreements were resolved by consultation with the senior author (Y-QZ). We extracted the following data from each eligible systematic review with meta-analysis: name of the first author, year of publication, number of total studies, number of RCTs for meta-analysis, interventions, details of interventions, meta-analysis metrics and effect.

Assessment of methodological quality

In our study, 6 trained authors (LM, W-SZ, RW, S-SH, S-YC, and L-HS) matched in pairs, and independently assessed the methodological quality of qualified systematic reviews and meta-analyses using a Measurement Tool to Assess Systematic Reviews (AMSTAR) [17]. Discrepancies were resolved through discussion with a senior author (Y-QZ). As a valid and dependable measurement tool in assessing the quality of systematic reviews with meta-analyses, AMSTAR assesses quality based on 11 aspects including a literature search, literature inclusion, data extraction, statistical analysis, and bias evaluation. The AMSTAR score was graded as high [8–11], moderate [4–7], and low (0–3) quality [17].

Data analysis

All analyses were performed by R software, x64 4.5.2. Heterogeneity was assessed using the I² statistic [18]. If the result of the heterogeneity test was P > 0.05, it was considered that the multiple literature studies were homogeneous, and the fixed-effect model was selected for the analysis; conversely, it indicated that there was heterogeneity among the literatures, and the random-effect model was adopted. A random-effects model accounted for variation between studies, as this can provide more conservative results than a fixed-effects model [19]. We recalculated the adjusted summary effect size estimates and corresponding 95% CIs by using a random-effects model, which considers heterogeneity between studies [20]. Besides, subgroup analyses were conducted based on different types of interventions, such as peer-based intervention, self-management Intervention and technology-based intervention. Publication bias was analyzed using the traditional funnel plot and Egger test [21]. All statistical analysis were two sides, and p < 0.05 is considered statistically significant in this study.

Results

Literature review

As shown in Fig. 1, we retrieved 3,080 records from 4 electronic databases, removed 1,760 duplicates and 146 articles were identified for detailed evaluation of full-text screening. We further excluded 134 articles based on the inclusion and exclusion criteria, with the reasons for exclusion detailed in Fig. 1. Ultimately, 12 articles were eligible to be included in the present UR [3, 22–32].

Fig. 1.

Preferred reporting items for systematic reviews and meta-analyses flow chart for study inclusion

Characteristics of the included meta-analyses

The eligible 12 articles described 294 meta-analyses, which estimated three different intervention models associated with the outcomes related to HbA1c. Detailed information is presented in Table 1. These meta-analyses were published from 2020 to 2025. There are 5 articles (41.67%) reporting fewer than 15 studies, 4 articles (33.33%) between 15 and 30 studies and 3 articles (25.00%) with more than 30 studies. The number of participants ranged from 121 to 7,342 in individual studies. Of the 12 meta-analyses, 1 article focused exclusively on peer-based intervention, while 7 articles centered on self-management Intervention and 4 articles delved into technology-based intervention respectively.

Table 1.

Characteristics summary of all included reviews

Study: Author (Year)	No of studies	n = RCTs for meta-analysis	Participants	Intervention	Outcomes
Peer-based intervention
Azmiardi A (2025)	12	RCT(n = 12)	1896	Individual intervention; Group intervention; A combination of both	Significant reduction in HbA1c MD -0.41% (95% CI: -0.69% to -0.13%)
Self-management Intervention
Khavere S (2025)	10	RCT(n = 4) QED (n = 1)	121	Digital intervention; Community centre intervention; Hospital intervention; Digital +home visits intervention	Non-significant MD of -0.10% (95% CI: -0.72% to 0.52%)
Rahimi G R M (2022)	17	RCT(n = 8)	1155	Aerobic exercise interventions; Combined interventions; Exercise and dietary advice interventions	Significant reduction in HbA1c MD -0.51% (95% CI: -0.97% to -0.04%)
Kim J (2021)	36	RCT(n = 33)	5639	Dietary education interventions; Exercise therapy interventions; Psychosocial therapy interventions	Significant reduction in HbA1c MD -0.42% (95% CI: -0.53% to -0.31%)
O’Donoghue G (2021)	25	RCT(n = 19)	5232	Self-management education interventions; Structured Diet/exercise/combined interventions	Significant reduction in HbA1c MD -0.63% (95% CI: -0.88% to -0.38%)
Shirvani T (2021)	19	RCT(n = 6)	6416	community-based educational interventions	Significant reduction in HbA1c MD -0.15% (95% CI: -0.28% to -0.03%)
A.Hildebrand J (2020)	18	RCT(n = 18)	3396	Individual education interventions; Group approach interventions; A combination of both	Significant reduction in HbA1c MD -0.240% (95% CI: -0.345% to -0.135%)
Siopis G (2020)	14	RCT(n = 13)	2511	Nutrition therapy interventions (delivered by dietitians to nutrition advice)	Significant reduction in HbA1c MD -0.47% (95% CI: -0.92% to -0.02%)
Technology-based intervention
Versluis A (2025)	43	Parallel RCT(n = 42)	6364	mHealth interventions	Significant reduction in HbA1c MD -0.30% (95% CI: -0.41% to -0.19%)
Kerr D (2024)	28	RCT(n = 20)	Not mentioned	digital interventions	Significant reduction in HbA1c MD -0.31% (95% CI: -0.45% to -0.16%)
De Groot J (2021)	43	RCT(n = 43)	6932	Text message interventions; Mobile app interventions; Interactive telephone interventions; Internet server/computer network interventions; Website interventions; Video conference interventions; Device or tablet interventions	Significant reduction in HbA1c MD -0.486% (95% CI: -0.561% to -0.410%)
Robson N (2021)	29	RCT(n = 21)	7342	Telemonitoring interventions; mHealth interventions; Virtual Consultation interventions; Telephone Communication interventions; Video Education interventions	Significant reduction in HbA1c MD -0.18% (95% CI: -0.35% to -0.01%)

RCT, Randomized Controlled trail; QED, Quasi Experimental Design; MD, Mean Deviation

Summary findings of the included meta-analyses

Almost all (11/12) reviews reported significant reduction of HbA1c for people with diabetes. The mean reduction in HbA1c was 0.34% (95% CI: −0.43% to − 0.25%). High heterogeneity, quantified by an I² of 73.1%, was present, likely due to differences in self-management educational interventions, starting HbA1c levels, lengths of follow-up, and educational elements within these research works (Fig. 2). The publication bias of 12 meta-analyses was acceptable. The details of publication bias assessment can be found in Supplementary Figures S1–S2.

Fig. 2.

Forest plot of the meta-analysis of HbA1c of the included reviews

Subgroup analysis results of the included meta-analyses

Only 1 article describe the associations between peer intervention and reduction in HbA1c (ES = − 0.41%, 95% confidence interval [CI]: − 0.69% to − 0.13%) (Fig. 3). The main interventions for peer intervention including individual intervention and group intervention, as well as the combination of both.

Fig. 3.

Forest plot of subgroup analysis

The majority (6 out of 7 reviews) described studies with self-management intervention with a reduction of HbA1c. The mean reduction in HbA1c was 0.35% (95% CI: − 0.50% to − 0.20%; I² = 68.7%) (Fig. 3). Self-management interventions can be broadly divided into diet and exercise. In terms of diet, interventions can be categorized as dietary education interventions [24] and nutrition therapy and dietary advice interventions [30]. For exercise, the intervention included aerobic exercise interventions, exercise therapy interventions and resistance training interventions [23]. Furthermore, some articles also cover the comprehensive intervention involving both exercise and dietary modifications [25]. Of note, only 1 article addressed the use of psychological interventions to lower HbA1c [24].

The 4 included articles on technology-based intervention all reduced HbA1c levels, with a reduction of 0.33% (95% CI: − 0.46% to − 0.20%; I² = 82.9%) (Fig. 3). Technology-based intervention included either stand-alone interventions or combinations of mHealth, digital, text message, mobile app, interactive telephone, internet server/computer network, website, video conference, device or tablet, telemonitoring, virtual consultation, telephone communication and internet-transmitted.

Methodological quality of the meta-analyses

Using the AMSTAR tool [17], 6 (50.00%) articles [3, 22, 24, 25, 27, 30] were categorized as high quality, whereas 6 (50.00%) articles [23, 26, 28, 29, 31, 32] were categorized as moderate quality (Fig. 4). The median number of the AMSTAR score was 7.5 (range from 5 to 9). The main reasons for lower AMSTAR scores were that including meta-analyses did not provide a list of studies (included and excluded), and did not consider the scientific quality of primary studies in preparing their conclusions and recommendations, and did not provide an “a priori” design.

Fig. 4.

Assessment of the methodological quality of the included studies

Discussion

In this study, we provided a comprehensive overview of reported associations between DSME and HbA1c levels by incorporating evidence from 12 studies. Our meta-analysis showed that DSME interventions are associated with a statistically significant reduction of 0.34% mean HbA1c compared with standard care in RCTs.

We found only 1 article describe the associations between peer interventions and reduction in HbA1c. The results might same as previous findings. The evidence suggests that studies on peer support integrated with DSME significantly reduced HbA1c levels and improved glycemic control among participants in the intervention groups [3, 33]. However, our findings on peer interventions must be interpreted with caution due to the small number of studies that offered peer interventions (n = 1). This area warrants continued exploration as advancements in various technologies emerge, and its full potential has yet to be fully realized.

In addition, subgroup analysis also revealed a reduction in HbA1c through self-management intervention. The observed 0.35% improvement in glycemic control, as measured by HbA1c levels, was relatively appropriate. This result is in line with prior evidence [27, 34]. However, Terranova et al. [11] reported that lifestyle intervention showed a non-significant pooled trend toward a reduction in HbA1c. The high heterogeneity of HbA1c changes (I² = 68.7%) might be the reason for the significant differences observed across results. This means that more research will be needed in the future to confirm this conclusion.

Moreover, we also reported that technology-based interventions reduced HbA1c levels. Our findings are consistent with the previous umbrella review [10], which included articles published before March 2022, and concluded that virtual care significantly improves clinical outcomes in people with type 2 diabetes, primarily affecting HbA1c. Research indicates that technology-based interventions may be particularly effective among older adults, especially those aged 50 years and older [35, 36], as well as individuals with type 2 diabetes who exhibit elevated baseline HbA1c levels [37], while another umbrella review reported that technology-based interventions demonstrated a greater effect among younger individuals with type 2 diabetes, specifically those aged 41 to 50 years [38]. Further research is needed to generate evidence on whether the observed effects in different age groups are related to digital health literacy and technology.

The principal strengths were that the present study was the most comprehensive systematic review to estimate the relationship between DSME and the HbA1c levels. Besides, only RCTs were included in this umbrella review to ensure high validity of the results. Nevertheless, some limitations of this study should be recognized. First of all, although a comprehensive and piloted search strategy was developed using a broad set of keywords, it is possible that some relevant studies were overlooked. Secondly, the outcomes of interest in this study are limited to HbA1c; however, existing evidence supports HbA1c as the recognized standard for assessing glycemic control over the preceding 12-week period [39]. Third, we excluded studies with alternative experimental designs that may have provided additional insights into this research question, as many of these studies may have been conducted in real-world DSME program settings, where RCTs are often uncommon and impractical [7]. Fourth, the present study focused exclusively on the educational intervention component and did not incorporate the continuous support dimension emphasized in the Diabetes Self-Management Education and Support (DSMES) framework. This limitation has been supplemented and clarified in the revised manuscript. Specifically, the research design that solely focuses on educational interventions may restrict the generalizability of our findings to broader DSMES-based clinical practices. Future studies are needed to further explore the combined effects of education and continuous support on diabetes self-management outcomes.

Conclusions

This umbrella review consolidates the existing evidence demonstrating that DSME has a significant positive impact on clinical outcomes, particularly in relation to HbA1c levels, among individuals with type 2 diabetes. Future research could integrate multimodal DSME to improve its efficacy across a wide range of health outcomes and ensure adaptation to the needs of diverse populations.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

LM and W-SZ contributed equally to this work. LM, W-SZ, RW, YC, and Y-QZ contributed to the study design. LM, W-SZ, RW, S-SH, S-YC, and L-HS collection of data. LM, W-SZ, RW, S-SH, S-YC, and L-HS analysis of data. LM, W-SZ, RW, S-SH, S-YC, L-HS, YC, and Y-QZ wrote the first draft of the manuscript and edited the manuscript. All authors read and approved the final manuscript. YC and Y-QZ contributed equally to this work.

Funding

This work was supported by the 2024 Research Project of Shenzhen Hospital of Guangzhou University of Chinese Medicine (Futian) (No. GZYSY2024040 to LM).

Data availability

All the data supporting the conclusions of this article are included within the article and its supplementary files.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.